metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - wikiann
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: arabic2023_ner_model
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: wikiann
          type: wikiann
          config: ar
          split: validation
          args: ar
        metrics:
          - name: Precision
            type: precision
            value: 0.825519413120349
          - name: Recall
            type: recall
            value: 0.8312960600907029
          - name: F1
            type: f1
            value: 0.8283976661870758
          - name: Accuracy
            type: accuracy
            value: 0.9048229813780053

arabic2023_ner_model

This model is a fine-tuned version of distilbert-base-uncased on the wikiann dataset. It achieves the following results on the evaluation set:

Loss: 0.3950
Precision: 0.8255
Recall: 0.8313
F1: 0.8284
Accuracy: 0.9048

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1594	1.0	1250	0.4149	0.8145	0.8133	0.8139	0.8974
0.116	2.0	2500	0.3950	0.8255	0.8313	0.8284	0.9048

Framework versions

Transformers 4.27.1
Pytorch 2.0.1+cu118
Datasets 2.9.0
Tokenizers 0.13.3